Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects high-volume retrieval of secrets by a single principal within a one-hour window across AWS Secrets Manager, Azure Key Vault, and Google Cloud Secret Manager. This behavior is indicative of bulk credential harvesting or automated sensitive data exfiltration following unauthorized access or privilege escalation in cloud environments.
Detects network activity associated with the TA419 threat group's 'Frameless' Browser-in-the-Browser (BitB) Adversary-in-the-Middle (AitM) phishing campaign. The rules identify unauthorized OAuth2 authorization requests to non-Microsoft hosts that match known malicious client IDs, the delivery of BitB overlay scripts, and the subsequent execution of malicious scripts correlated with OneDrive-themed lookalike redirects, indicating a credential theft attempt.
Detects when an AI agent or assistant process attempts to access sensitive local configuration or credential files (e.g., .aws/credentials, .env, id_rsa, service account keys, .netrc) that typically fall outside the scope of its intended function. This behavior is indicative of unauthorized access, potentially due to prompt injection or malicious exploitation of the agent's file system permissions.
Detects credential stuffing in Entra ID sign-in logs: one source IP failing against many distinct accounts within an hour, then succeeding on a small number of them.
Detects credential stuffing in Entra ID sign-in logs: one source IP failing against many distinct accounts within an hour, then succeeding on a small number of them.
Detects network activity associated with the TA419 threat group's 'Frameless' Browser-in-the-Browser (BitB) Adversary-in-the-Middle (AitM) phishing campaign. The rules identify unauthorized OAuth2 authorization requests to non-Microsoft hosts that match known malicious client IDs, the delivery of BitB overlay scripts, and the subsequent execution of malicious scripts correlated with OneDrive-themed lookalike redirects, indicating a credential theft attempt.
Detects instances where a process that is not a recognized web browser (chrome, msedge, firefox, brave, opera, or explorer) accesses multiple unique browser cookie files. This is a common pattern for credential harvesting malware attempting to steal browser sessions.
Detects unauthorized process activity that reads, copies, archives, or transmits the NetScaler ns.conf configuration file. This file contains highly sensitive information, including AAA/LDAP bind credentials and certificates. Adversaries may stage or exfiltrate this file following the exploitation of vulnerabilities (such as CVE-2026-88771 or CVE-2026-88772) on NetScaler appliances to facilitate further network compromise.
Detects runtime processes associated with AI agents or scripting environments (e.g., python, node, deno) accessing sensitive credential storage paths and files, such as SSH keys, cloud provider configuration directories, and local environment variables, which may indicate credential exfiltration.
Detects anomalous GCP IAM Service Account Credential API calls where a principal impersonates multiple distinct service accounts within a short time window. This behavior often indicates a credential delegation chain or a multi-stage privilege escalation attempt within a GCP environment.
This rule detects potential password spraying activity by monitoring for a single source IP address that targets multiple distinct user accounts with failed authentication attempts. It specifically identifies 'low-and-slow' patterns that may attempt to evade standard lockout thresholds, and flags cases where legacy authentication protocols are utilized, a technique commonly employed to bypass Multi-Factor Authentication (MFA).
Detects instances where a user account associated with a detected infostealer malware infection (e.g., Redline, Raccoon, Vidar) successfully authenticates to a cloud service from a suspicious network location, such as a VPN, residential proxy, or Tor exit node, within 24 hours of the infection alert.
Detects the use of legitimate archival utilities (such as 7-Zip, WinRAR, tar) or scripting engines (PowerShell) to compress or package sensitive browser profile artifacts like 'Login Data', 'Cookies', or 'Local State'. This behavior is a common indicator of credential harvesting and data staging activity by infostealers prior to exfiltration.
Detects a sequence of authentication activity indicative of a credential stuffing attack. The rule identifies a burst of failed password-only authentication attempts followed by a successful authentication for the same account. It requires that the IP address used for the successful login overlaps with the set of IPs that initiated the failed attempts, effectively filtering out typical user noise and highlighting potentially malicious activity where an attacker has identified a valid credential.
Detects the use of standard Linux command-line utilities (e.g., cat, curl, vi) to read or reference sensitive cloud credential files or Kubernetes service account tokens. This behavior is a common indicator of credential theft or discovery activities following initial access to a Linux host.
Detects anomalous authentication patterns where a single source IP targets a high number of unique user accounts with failed authentication attempts over a rolling window. By filtering out SSO/Federated methods and specific service account patterns, this rule isolates low-and-slow brute-force or password-spraying activities designed to evade standard account lockout mechanisms.
Detects anomalous AWS activity combining bulk secret retrieval from AWS Secrets Manager using the BatchGetSecretValue API with subsequent authentication failures or validation errors in AWS STS. This behavior is indicative of automated credential harvesting pipelines or agentic frameworks rapidly enumerating and testing stolen credentials.
Detects registration of a new actor-controlled MFA authentication method (phone, authenticator app, or software OTP token) via an Update user operation, matching the NO_DEVICE/NO_DEVICE_TOKEN/SoftwareTokenActivated pattern used to convert temporary compromise into durable identity persistence
Detects the use of standard Linux command-line utilities (e.g., cat, curl, vi) to read or reference sensitive cloud credential files or Kubernetes service account tokens. This behavior is a common indicator of credential theft or discovery activities following initial access to a Linux host.
Detects instances where a long-dormant Entra ID (Azure AD) service principal is reactivated shortly after having a new credential added or a new app role assigned. This activity is a potential persistence technique where an attacker repurposes a legitimate, high-privileged, but unused service principal to blend in as legacy automation.
This rule detects potential adversary-in-the-middle (AiTM) session hijacking by monitoring non-interactive sign-in events. It identifies when a refresh token is redeemed from a device identifier that does not exist in the user's recent interactive sign-in history and from a geographical location different from the user's last known interactive session.
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